Description Usage Arguments Objects from the Class Slots Methods Author(s) Examples
A class for implementing quadratic discriminant analysis with joint precision matrix estimation using ridge fusion
1 2 | RidgeFusedQDA(...)
predict.RidgeFusedQDA(object,newdata,class=TRUE,...)
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... |
Optional Arguments |
object |
An object of RidgeFusedQDA |
newdata |
data to be predicted |
class |
if TRUE then predicted classes are returned if false QDA scores are returned |
Objects can be created by calls of the form RidgeFusedQDA(...)
.
Omega
:Object of class "list"
~~
Means
:Object of class "list"
~~
Pi
:Object of class "vector"
~~
lambda1
:Object of class "numeric"
~~
lambda2
:Object of class "numeric"
~~
signature(object = "RidgeFusedQDA")
: ...
signature(x = "RidgeFusedQDA")
: ...
Brad Price
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | showClass("RidgeFusedQDA")
## Creating a toy example with 5 variables
library(mvtnorm)
set.seed(526)
p=5
Sig1=matrix(0,p,p)
for(j in 1:p){
for(i in j:p){
Sig1[j,i]=.7^abs(i-j)
Sig1[i,j]=Sig1[j,i]
}
}
Sig2=diag(c(rep(2,p-5),rep(1,5)),p,p)
X1=rmvnorm(100,rep(2*log(p)/p,p),Sig1)
Y=rmvnorm(100,,Sig2)
Z=list(X1,Y)
A2=FusedQDA(Z,10,10,scale=TRUE)
names(A2)
Class=predict(A2,X1,class=TRUE)
Score=predict(A2,X1,class=FALSE)
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